In sports, people love to categorize players by their playing style. For example, in hockey, people distinguish defensemen as offensive or defensive, or the rare all-around defensemen. In this week’s installment of My Model Monday, I look to create mathematical groupings of NHL defensemen using 2017-2018 NHL data.
In this week’s My Model Monday, I compare our 2018 NHL Season Simulation results to the current NHL futures odds to see where our model is differing from betting markets. Continue reading My Model Monday: NHL Playoff Futures
Below is a table of our 2017 NHL Season Simulation and Elo Ratings. The Projected Win Totals and Overtime Losses are used to calculate the Projected Point Totals. The column SD represents the standard deviation of point totals for each team. Rankings are based on Projected Point Totals generated from a Markov Chain Monte Carlo Simulation. Tune in to this post for weekly updates to these figures throughout the rest of the 2017 NHL Season.
In my first ever My Model Monday, I wanted to get back to my roots: ice hockey and St. Olaf College. For those who don’t know, I used to play ice hockey (sometimes) and did so at St. Olaf College; therefore, I figured it would be fun to bring some analysis to a sport and level that is rarely covered: Division III Men’s Ice Hockey.
Continue reading My Model Monday: DIII Men’s MIAC Hockey Rankings
Well, the Nashville Predators have done it again. Against all odds (or at least the Model’s odds), the Preds have punched their ticket to the Stanley Cup Finals, where they will meet the reigning Stanley Cup Champion Pittsburgh Penguins. While Nashville has continually defied logic by beating the Model, in their toughest test yet, the Model has them as heavy underdogs once again.
Round 2 of the NHL Playoffs was filled with sweat, blood, and tears. As suggested by the Model, we saw a couple of game 7s, one of which saw Alexander Ovechkin and the Washington Capitals fall victim to the Sidney Crosby-led Pittsburgh Penguins (or should I say the Jake Guentzel led Pittsburgh Penguins). Anyway, the Model went 2 for 4 in round 2 predictions, although the Capitals-Penguins series was pretty much 50-50, and the Model actually had the Penguins with a better chance of winning the cup than the Capitals. The Predators continued to outsmart the Model by dismantling the Blues in 6. It is now conceivable that the Model may not be properly capturing the true ability of the Predators. We will put that theory to the test once again as the Predators remain underdogs against the Ducks, from the Model’s point of view.
The Wild bowed out early yet again, the Flames looked every bit the team with a <1% chance of winning the cup, and the Model went 7 for 8 in first round series predictions. The big (and only) miss came on the heels of the Chicago Blackhawks, who apparently decided they would rather be golfing than playing hockey. Despite the addition of Bruce Boudreau, the superstar-less Minnesota Wild proved once again that they lack a star goal scorer needed to make a deep run in the playoffs, such as a Vladimir Tarasenko. While Round 1 presented zero game sevens, the Model predicts we might see a nail biter or two in the second round.
Using the average probability from our NHL Playoff Models, we have run a large-scale simulation for the 2017 NHL Playoffs. This allows us to estimate how often each team makes the semifinals, conference championship, Stanley Cup Finals, and ultimately win the Stanley Cup. In order to do this, we took each series and generated a random number between 0 and 1. If the random number is less than our predicted probability of the home team winning, then we advance the home team as the winner of that series (and vice versa if the random number is greater than our probability). For example, Model 1 has a probability of 0.74 that the Chicago Blackhawks will beat the Nashville Predators. If the random number is 0.95, we would pick the Predators to advance, and if the random number is 0.55, we would pick the Blackhawks to advance. This methodology was applied to the entire NHL Playoff bracket, generating 10,000 brackets and 10,000 Stanley Cup champions.
Our NHL playoff models use team-level and individual player statistics to predict the probability that a team will win a given series. We build our bracket by advancing the team with the higher win probability for each series. For example, this year, our models give the Chicago Blackhawks a 74% chance of winning their first round series against the Nashville Predators, so we advanced the Blackhawks to the following round in our bracket. While we utilize multiple models to generate predictions for each series, the bracket below represents the average probability of all models for the 2017 NHL Playoffs. For more information on our NHL Playoff Models, see our methodology article here.
The NHL Playoff Model uses team-level and individual player statistics to predict the probability that a given team will win a series (rather than predicting each game individually). Theoretically, one would think predicting a winner of a series would be easier than predicting the probability a team wins a single game, but many things can happen throughout the course of a series, most significantly, injuries that make predicting the series winner difficult.